Spaces:
Running
Running
| import json | |
| import time | |
| from typing import Dict, List, Tuple | |
| import gradio as gr | |
| import streamlit as st | |
| import streamlit_chat | |
| from huggingface_hub import InferenceClient, hf_hub_url, cached_download | |
| import git | |
| from langchain_community.llms import HuggingFaceHub | |
| from langchain_community.chains import ConversationChain | |
| from langchain_community.memory import ConversationBufferMemory | |
| from langchain_community.chains.question_answering import load_qa_chain | |
| from langchain_community.utils import CharacterTextSplitter | |
| from transformers import BertTokenizerFast | |
| # --- Constants --- | |
| MODEL_NAME = "google/flan-t5-xl" # Consider using a more powerful model like 'google/flan-t5-xl' | |
| MAX_NEW_TOKENS = 2048 # Increased for better code generation | |
| TEMPERATURE = 0.7 | |
| TOP_P = 0.95 | |
| REPETITION_PENALTY = 1.2 | |
| # --- Model & Tokenizer --- | |
| def load_model_and_tokenizer(): | |
| model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto") # Use 'auto' for optimal device selection | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| return model, tokenizer | |
| model, tokenizer = load_model_and_tokenizer() | |
| PRETRAINED_MODEL_NAME = "distilbert-base-uncased" | |
| model_path = os.path.join(os.getcwd(), PRETRAINED_MODEL_NAME) | |
| if not os.path.exists(model_path): | |
| raise FileNotFoundError("Pre-trained model weight directory {} doesn't exist".format(model_path)) | |
| else: | |
| print("Found Pre-trained Model at:", model_path) | |
| tokenizer = GPT2Tokenizer.from_pretrained(model_path) | |
| # Download the DistilBERT tokenizer (~3 MB) | |
| DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased').save_pretrained('./cache/distilbert-base-uncased-local') | |
| # --- Agents --- | |
| agents = { | |
| "WEB_DEV": { | |
| "description": "Expert in web development technologies and frameworks.", | |
| "skills": ["HTML", "CSS", "JavaScript", "React", "Vue.js", "Flask", "Django", "Node.js", "Express.js"], | |
| "system_prompt": "You are a web development expert. Your goal is to assist the user in building and deploying web applications. Provide code snippets, explanations, and guidance on best practices.", | |
| }, | |
| "AI_SYSTEM_PROMPT": { | |
| "description": "Expert in designing and implementing AI systems.", | |
| "skills": ["Machine Learning", "Deep Learning", "Natural Language Processing", "Computer Vision", "Reinforcement Learning"], | |
| "system_prompt": "You are an AI system expert. Your goal is to assist the user in designing and implementing AI systems. Provide code snippets, explanations, and guidance on best practices.", | |
| }, | |
| "PYTHON_CODE_DEV": { | |
| "description": "Expert in Python programming and development.", | |
| "skills": ["Python", "Data Structures", "Algorithms", "Object-Oriented Programming", "Functional Programming"], | |
| "system_prompt": "You are a Python code development expert. Your goal is to assist the user in writing and debugging Python code. Provide code snippets, explanations, and guidance on best practices.", | |
| }, | |
| "CODE_REVIEW_ASSISTANT": { | |
| "description": "Expert in code review and quality assurance.", | |
| "skills": ["Code Style", "Best Practices", "Security", "Performance", "Maintainability"], | |
| "system_prompt": "You are a code review expert. Your goal is to assist the user in reviewing and improving their code. Provide feedback on code quality, style, and best practices.", | |
| }, | |
| } | |
| # --- Session State --- | |
| if "workspace_projects" not in st.session_state: | |
| st.session_state.workspace_projects = {} | |
| if "chat_history" not in st.session_state: | |
| st.session_state.chat_history = [] | |
| if "active_agent" not in st.session_state: | |
| st.session_state.active_agent = None | |
| if "selected_agents" not in st.session_state: | |
| st.session_state.selected_agents = [] | |
| if "current_project" not in st.session_state: | |
| st.session_state.current_project = None | |
| # --- Helper Functions --- | |
| def add_code_to_workspace(project_name: str, code: str, file_name: str): | |
| if project_name in st.session_state.workspace_projects: | |
| st.session_state.workspace_projects[project_name]['files'].append({'file_name': file_name, 'code': code}) | |
| return f"Added code to {file_name} in project {project_name}" | |
| else: | |
| return f"Project {project_name} does not exist" | |
| def terminal_interface(command: str, project_name: str): | |
| if project_name in st.session_state.workspace_projects: | |
| result = subprocess.run(command, cwd=project_name, shell=True, capture_output=True, text=True) | |
| return result.stdout + result.stderr | |
| else: | |
| return f"Project {project_name} does not exist" | |
| def get_agent_response(message: str, system_prompt: str): | |
| llm = HuggingFaceHub(repo_id=MODEL_NAME, model_kwargs={"temperature": TEMPERATURE, "top_p": TOP_P, "repetition_penalty": REPETITION_PENALTY, "max_length": MAX_NEW_TOKENS}) | |
| memory = ConversationBufferMemory() | |
| conversation = ConversationChain(llm=llm, memory=memory) | |
| response = conversation.run(system_prompt + "\n" + message) | |
| return response | |
| def display_agent_info(agent_name: str): | |
| agent = agents[agent_name] | |
| st.sidebar.subheader(f"Active Agent: {agent_name}") | |
| st.sidebar.write(f"Description: {agent['description']}") | |
| st.sidebar.write(f"Skills: {', '.join(agent['skills'])}") | |
| def display_workspace_projects(): | |
| st.subheader("Workspace Projects") | |
| for project_name, project_data in st.session_state.workspace_projects.items(): | |
| with st.expander(project_name): | |
| for file in project_data['files']: | |
| st.text(file['file_name']) | |
| st.code(file['code'], language="python") | |
| def display_chat_history(): | |
| st.subheader("Chat History") | |
| html_string = "" | |
| for idx, message in enumerate(st.session_state.chat_history): | |
| if idx % 2 == 0: | |
| role = "User:" | |
| else: | |
| role = "Assistant:" | |
| html_string += f"<p>{role}</p>" | |
| html_string += f"<p>{message}</p>" | |
| st.markdown(html_string, unsafe_allow_html=True) | |
| def run_autonomous_build(selected_agents: List[str], project_name: str): | |
| st.info("Starting autonomous build process...") | |
| for agent in selected_agents: | |
| st.write(f"Agent {agent} is working on the project...") | |
| code = get_agent_response(f"Generate code for a simple web application in project {project_name}", agents[agent]['system_prompt']) | |
| add_code_to_workspace(project_name, code, f"{agent.lower()}_app.py") | |
| st.write(f"Agent {agent} has completed its task.") | |
| st.success("Autonomous build process completed!") | |
| def collaborative_agent_example(selected_agents: List[str], project_name: str, task: str): | |
| st.info(f"Starting collaborative task: {task}") | |
| responses = {} | |
| for agent in selected_agents: | |
| st.write(f"Agent {agent} is working on the task...") | |
| response = get_agent_response(task, agents[agent]['system_prompt']) | |
| responses[agent] = response | |
| combined_response = combine_and_process_responses(responses, task) | |
| st.success("Collaborative task completed!") | |
| st.write(combined_response) | |
| def combine_and_process_responses(responses: Dict[str, str], task: str) -> str: | |
| # This is a placeholder function. In a real-world scenario, you would implement | |
| # more sophisticated logic to combine and process the responses. | |
| combined = "\n\n".join([f"{agent}: {response}" for agent, response in responses.items()]) | |
| return f"Combined response for task '{task}':\n\n{combined}" | |
| # --- Streamlit UI --- | |
| st.title("DevToolKit: AI-Powered Development Environment") | |
| # --- Project Management --- | |
| st.header("Project Management") | |
| project_name = st.text_input("Enter project name:") | |
| if st.button("Create Project"): | |
| if project_name and project_name not in st.session_state.workspace_projects: | |
| st.session_state.workspace_projects[project_name] = {'files': []} | |
| st.success(f"Created project: {project_name}") | |
| elif project_name in st.session_state.workspace_projects: | |
| st.warning(f"Project {project_name} already exists") | |
| else: | |
| st.warning("Please enter a project name") | |
| # --- Code Editor --- | |
| st.subheader("Code Editor") | |
| if st.session_state.workspace_projects: | |
| selected_project = st.selectbox("Select project", list(st.session_state.workspace_projects.keys())) | |
| if selected_project: | |
| files = [file['file_name'] for file in st.session_state.workspace_projects[selected_project]['files']] | |
| selected_file = st.selectbox("Select file to edit", files) if files else None | |
| if selected_file: | |
| file_content = next((file['code'] for file in st.session_state.workspace_projects[selected_project]['files'] if file['file_name'] == selected_file), "") | |
| edited_code = st_ace(value=file_content, language="python", theme="monokai", key="code_editor") | |
| if st.button("Save Changes"): | |
| for file in st.session_state.workspace_projects[selected_project]['files']: | |
| if file['file_name'] == selected_file: | |
| file['code'] = edited_code | |
| st.success("Changes saved successfully!") | |
| break | |
| else: | |
| st.info("No files in the project. Use the chat interface to generate code.") | |
| else: | |
| st.info("No projects created yet. Create a project to start coding.") | |
| # --- Terminal Interface --- | |
| st.subheader("Terminal (Workspace Context)") | |
| if st.session_state.workspace_projects: | |
| selected_project = st.selectbox("Select project for terminal", list(st.session_state.workspace_projects.keys())) | |
| terminal_input = st.text_input("Enter a command within the workspace:") | |
| if st.button("Run Command"): | |
| terminal_output = terminal_interface(terminal_input, selected_project) | |
| st.code(terminal_output, language="bash") | |
| else: | |
| st.info("No projects created yet. Create a project to use the terminal.") | |
| # --- Chat Interface --- | |
| st.subheader("Chat with AI Agents") | |
| selected_agents = st.multiselect("Select AI agents", list(agents.keys()), key="agent_select") | |
| st.session_state.selected_agents = selected_agents | |
| agent_chat_input = st.text_area("Enter your message for the agents:", key="agent_input") | |
| if st.button("Send to Agents", key="agent_send"): | |
| if selected_agents and agent_chat_input: | |
| responses = {} | |
| for agent in selected_agents: | |
| response = get_agent_response(agent_chat_input, agents[agent]['system_prompt']) | |
| responses[agent] = response | |
| st.session_state.chat_history.append(f"User: {agent_chat_input}") | |
| for agent, response in responses.items(): | |
| st.session_state.chat_history.append(f"{agent}: {response}") | |
| st_chat(st.session_state.chat_history) # Display chat history using st_chat | |
| else: | |
| st.warning("Please select at least one agent and enter a message.") | |
| # --- Agent Control --- | |
| st.subheader("Agent Control") | |
| for agent_name in agents: | |
| agent = agents[agent_name] | |
| with st.expander(f"{agent_name} ({agent['description']})"): | |
| if st.button(f"Activate {agent_name}", key=f"activate_{agent_name}"): | |
| st.session_state.active_agent = agent_name | |
| st.success(f"{agent_name} activated.") | |
| if st.button(f"Deactivate {agent_name}", key=f"deactivate_{agent_name}"): | |
| st.session_state.active_agent = None | |
| st.success(f"{agent_name} deactivated.") | |
| # --- Automate Build Process --- | |
| st.subheader("Automate Build Process") | |
| if st.button("Automate"): | |
| if st.session_state.selected_agents and project_name: | |
| run_autonomous_build(st.session_state.selected_agents, project_name) | |
| else: | |
| st.warning("Please select at least one agent and create a project.") | |
| # --- Version Control --- | |
| st.subheader("Version Control") | |
| repo_url = st.text_input("Enter repository URL:") | |
| if st.button("Clone Repository"): | |
| if repo_url and project_name: | |
| try: | |
| git.Repo.clone_from(repo_url, project_name) | |
| st.success(f"Repository cloned successfully to {project_name}") | |
| except git.GitCommandError as e: | |
| st.error(f"Error cloning repository: {e}") | |
| else: | |
| st.warning("Please enter a repository URL and create a project.") | |
| # --- Collaborative Agent Example --- | |
| st.subheader("Collaborative Agent Example") | |
| collab_agents = st.multiselect("Select AI agents for collaboration", list(agents.keys()), key="collab_agent_select") | |
| collab_project = st.text_input("Enter project name for collaboration:") | |
| collab_task = st.text_input("Enter a task for the agents to collaborate on:") | |
| if st.button("Run Collaborative Task"): | |
| if collab_agents and collab_project and collab_task: | |
| collaborative_agent_example(collab_agents, collab_project, collab_task) | |
| else: | |
| st.warning("Please select agents, enter a project name, and specify a task.") | |
| # --- Display Information --- | |
| st.sidebar.subheader("Current State") | |
| st.sidebar.json(st.session_state) | |
| if st.session_state.active_agent: | |
| display_agent_info(st.session_state.active_agent) | |
| display_workspace_projects() | |
| display_chat_history() | |
| if __name__ == "__main__": | |
| st.sidebar.title("DevToolKit") | |
| st.sidebar.info("This is an AI-powered development environment.") |